{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "5LZkDfavuZfH"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # 导入评估指标函数\n",
    "import matplotlib.pyplot as plt  # 导入数据可视化库 Matplotlib\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "v2DrT_Fluhmo"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9tgxxH2ZunW4",
    "outputId": "e600c7ea-d8db-40e7-e9aa-5ad4ccd574ea"
   },
   "outputs": [],
   "source": [
    "# scale train and test data to new feature range[0, 1]将训练和测试数据缩放到新的特征范围[0，1]\n",
    "def scale(train, test):\n",
    "\t# fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n",
    "\n",
    "def scaled(X, y):\n",
    "    max_train = np.max(X, axis=0) #标准化\n",
    "    min_train = np.min(X, axis=0)\n",
    "    X = 0.0 + (X - min_train) / (max_train - min_train)\n",
    "    max_targets = np.max(y, axis=0)\n",
    "    min_targets = np.min(y, axis=0)\n",
    "    y = 0.0 + (y - min_targets) / (max_targets - min_targets)\n",
    "    return X, y, max_train, min_train, max_targets, min_targets\n",
    "\n",
    "def inscaled(ypred):\n",
    "    y_pred = ypred * (max_targets - min_targets) + min_targets\n",
    "    return y_pred\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IX7fxTzQviVT",
    "outputId": "e0a865de-e3a3-42ef-94ec-2ede85635bd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       radiation  air temperature  air pressure  humidity  actual power/MW\n",
      "0            0.0            -14.1       908.672    81.997              0.0\n",
      "1            0.0            -14.6       908.672    82.597              0.0\n",
      "2            0.0            -14.4       908.672    82.897              0.0\n",
      "3            0.0            -14.8       908.672    82.997              0.0\n",
      "4            0.0            -14.8       909.172    82.897              0.0\n",
      "...          ...              ...           ...       ...              ...\n",
      "35021        0.0             -4.8       899.600    54.900              0.0\n",
      "35022        0.0             -5.9       900.100    55.800              0.0\n",
      "35023        0.0             -6.5       900.100    57.900              0.0\n",
      "35024        0.0             -7.4       900.100    59.400              0.0\n",
      "35025        0.0             -8.0       900.100    62.700              0.0\n",
      "\n",
      "[35026 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('PVDATA.csv')\n",
    "# 删除时间列\n",
    "df = df.drop(columns=['TIME'])\n",
    "# 目标列\n",
    "target_column = 'actual power/MW'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "VkRMpjH3vsxV",
    "outputId": "0cefe31f-2a9b-4ad4-9a0e-edf704532cfa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.    -14.1   908.672  81.997]\n",
      " [  0.    -14.6   908.672  82.597]\n",
      " [  0.    -14.4   908.672  82.897]\n",
      " ...\n",
      " [  0.     -6.5   900.1    57.9  ]\n",
      " [  0.     -7.4   900.1    59.4  ]\n",
      " [  0.     -8.    900.1    62.7  ]]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=target_column).values  # 从 DataFrame `df` 中去除目标列 `target_column`，并将剩余的列转换为数组赋值给变量 `X`\n",
    "\n",
    "y = df[target_column].values  # 从 DataFrame `df` 中选择目标列 `target_column`，并将其转换为数组赋值给变量 `y`\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "AELPEuNEvvIu",
    "outputId": "4580e7bf-d929-4a5b-f844-83a7dd037c09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.70982043 -0.1581825  -0.25278188  0.63564631]\n",
      " [ 0.70982043  1.         -0.59307447 -0.14555079  0.57492931]\n",
      " [-0.1581825  -0.59307447  1.         -0.0128098  -0.2217157 ]\n",
      " [-0.25278188 -0.14555079 -0.0128098   1.         -0.29023335]\n",
      " [ 0.63564631  0.57492931 -0.2217157  -0.29023335  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "X, y, max_train, min_train, max_targets, min_targets = scaled(X, y)#标准化\n",
    "# 计算皮尔逊相关系数矩阵\n",
    "corr = np.corrcoef(X,y,rowvar=False)\n",
    "print(corr)\n",
    "# 计算每个特征的相关性权重\n",
    "weights = np.abs(corr[-1, :-1])\n",
    "# 对数据进行加权\n",
    "X = np.multiply(X, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "9FJ0l2uyv-c_",
    "outputId": "a1529b5a-fa6a-43e1-aceb-6071052092be"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [ 'radiation','air temperature', 'air pressure','humidity','actual power/MW']\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={\"size\": 12},\n",
    "            xticklabels=feature_names, yticklabels=feature_names,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "GMwtGRGuwAYo",
    "outputId": "6e59772b-496a-4fea-a2d8-4217b1861052"
   },
   "outputs": [],
   "source": [
    "'''定义一个函数用于创建数据集，输入参数包括:\n",
    "dataset: 数据集\n",
    "look_back: 回溯长度，即用多少个时间步作为输入预测下一个时间步'''\n",
    "def creat_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):# 循环遍历数据集\n",
    "        a = dataset[i: (i + look_back)]# 提取回溯长度的数据作为输入\n",
    "        dataX.append(a)# 将该输入数据添加到输入数据集\n",
    "        dataY.append(dataset[i + look_back])# 将下一个时间步的数据添加到输出数据集\n",
    "    return np.array(dataX), np.array(dataY)# 将输入输出数据集转换为numpy数组并返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "AF2Q7kW8wGKz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28020,)\n"
     ]
    }
   ],
   "source": [
    "dataset = y\n",
    "train_size = int(len(dataset) * 0.8)  # 计算训练集大小，将数据集长度的 80% 转换为整数\n",
    "test_size = len(dataset) - train_size  # 计算测试集大小，将剩余的数据作为测试集\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]  # 将数据集划分为训练集和测试集，使用切片操作实现\n",
    "print(\"Train X shape:\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tG_r8zTO0LiE"
   },
   "outputs": [],
   "source": [
    "look_back = 1\n",
    "train_X, train_Y = creat_dataset(train, look_back)  # 根据训练集数据和滑动窗口大小创建训练集的输入序列和输出值\n",
    "test_X, test_Y = creat_dataset(test, look_back)  # 根据测试集数据和滑动窗口大小创建测试集的输入序列和输出值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ads7b1Av0KD5"
   },
   "outputs": [],
   "source": [
    "# 调用GPU加速\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a8cA0UEBwo3N",
    "outputId": "175cefe4-156a-402b-93bc-1d6d98c116fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28018, 1)\n",
      "Train Y shape: (28018,)\n",
      "Test X shape: (7004, 1)\n",
      "Test Y shape: (7004,)\n"
     ]
    }
   ],
   "source": [
    "# Ensure the sizes of training and testing sets\n",
    "print(\"Train X shape:\", train_X.shape)\n",
    "print(\"Train Y shape:\", train_Y.shape)\n",
    "print(\"Test X shape:\", test_X.shape)\n",
    "print(\"Test Y shape:\", test_Y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "_mq48nepzY_I"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
    "save_best = ModelCheckpoint(\"best_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "TBrMgLrIwsGv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.sequential.Sequential object at 0x0000013BC7CFF370>\n",
      "Epoch 1/50\n",
      "876/876 [==============================] - 6s 5ms/step - loss: 0.1437 - val_loss: 0.0032 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0124 - val_loss: 0.0031 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0091 - val_loss: 0.0034 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0070 - val_loss: 0.0044 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0071 - val_loss: 0.0033 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "871/876 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0076 - val_loss: 0.0035 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0051 - val_loss: 0.0032 - lr: 2.5000e-04\n",
      "Epoch 8/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0053 - val_loss: 0.0030 - lr: 2.5000e-04\n",
      "Epoch 9/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0055 - val_loss: 0.0029 - lr: 2.5000e-04\n",
      "Epoch 10/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0057 - val_loss: 0.0065 - lr: 2.5000e-04\n",
      "Epoch 11/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0056 - val_loss: 0.0030 - lr: 2.5000e-04\n",
      "Epoch 12/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0055 - val_loss: 0.0069 - lr: 2.5000e-04\n",
      "Epoch 13/50\n",
      "869/876 [============================>.] - ETA: 0s - loss: 0.0054\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0054 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 14/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0050 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 15/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 16/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 17/50\n",
      "874/876 [============================>.] - ETA: 0s - loss: 0.0049\n",
      "Epoch 17: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 18/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 19/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 20/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 21/50\n",
      "872/876 [============================>.] - ETA: 0s - loss: 0.0048\n",
      "Epoch 21: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 22/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0030 - lr: 1.0000e-05\n",
      "Epoch 23/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0029 - lr: 1.0000e-05\n",
      "Epoch 24/50\n",
      "876/876 [==============================] - 5s 6ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 25/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 26/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 27/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 28/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0029 - lr: 1.0000e-05\n",
      "Epoch 29/50\n",
      "876/876 [==============================] - 5s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 30/50\n",
      "801/876 [==========================>...] - ETA: 0s - loss: 0.0049"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [20], line 12\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(tcn_model)\n\u001b[0;32m     10\u001b[0m tcn_model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# 编译模型，指定优化器和损失函数\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m \u001b[43mtcn_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_Y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtest_X\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_Y\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mreduce_lr\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_best\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\keras\\engine\\training.py:1564\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1556\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1557\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1558\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1561\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1562\u001b[0m ):\n\u001b[0;32m   1563\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1564\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1565\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1566\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateless_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m   2494\u001b[0m   (graph_function,\n\u001b[0;32m   2495\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1860\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1861\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1863\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1865\u001b[0m     args,\n\u001b[0;32m   1866\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1867\u001b[0m     executing_eagerly)\n\u001b[0;32m   1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    498\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    505\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    506\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    507\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m    508\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    511\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m    512\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[1;32mD:\\Software\\Anaconda\\envs\\qqqq\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.layers import Concatenate, Input\n",
    "from keras.models import Model\n",
    "from tcn import TCN, tcn_full_summary  # 从 tcn 模块中导入 TCN 类和 tcn_full_summary 函数\n",
    "\n",
    "\n",
    "\n",
    "tcn_model = Sequential([TCN(nb_filters=128, kernel_size=1, input_shape=(train_X.shape[1], 1)),\n",
    "                        Dense(1)])  # 添加 TCN 层和输出层到模型中\n",
    "print(tcn_model)\n",
    "tcn_model.compile(optimizer='adam', loss='mse')  # 编译模型，指定优化器和损失函数\n",
    "\n",
    "tcn_model.fit(train_X, train_Y, epochs=50, batch_size=32,validation_data=(test_X , test_Y),\n",
    "            callbacks=[reduce_lr , save_best])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "876/876 [==============================] - 1s 900us/step\n",
      "219/219 [==============================] - 0s 920us/step\n"
     ]
    }
   ],
   "source": [
    "# 对训练集进行预测\n",
    "ypred_train = tcn_model.predict(train_X)\n",
    "# 将训练集的预测结果转换为原始数据的范围\n",
    "ypred_train = inscaled(ypred_train)\n",
    "# 将训练集的真实标签转换为原始数据的范围\n",
    "y_train = inscaled(train_Y)\n",
    "\n",
    "# 对测试集进行预测\n",
    "ypred_test = tcn_model.predict(test_X)\n",
    "# 将测试集的预测结果转换为原始数据的范围\n",
    "ypred_test = inscaled(ypred_test)\n",
    "# 将测试集的真实标签转换为原始数据的范围\n",
    "y_test = inscaled(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wvkFS4KjwxIY",
    "outputId": "f6839368-9d52-419c-f04c-ce18572bcb8f"
   },
   "outputs": [],
   "source": [
    "y_test = y_test[0:len(ypred_test)-1]\n",
    "ypred_test = ypred_test[1:len(ypred_test),0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8OGCdy0MwzJm",
    "outputId": "77561502-eb1e-4fe3-edf2-3065c5185729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "RMSE 0.329 \n",
      "MAE 0.171 \n",
      "R2 0.998 \n",
      "MAPE 4.722 \n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "# 计算测试集的均方根误差（RMSE）\n",
    "testScore = math.sqrt(mean_squared_error(y_test, ypred_test))\n",
    "print('RMSE %.3f ' %(testScore))\n",
    "# 计算测试集的平均绝对误差（MAE）\n",
    "testScore = mean_absolute_error(y_test, ypred_test)\n",
    "print('MAE %.3f ' %(testScore))\n",
    "# 计算测试集的R平方值（R2）\n",
    "testScore = r2_score(y_test, ypred_test)\n",
    "print('R2 %.3f ' %(testScore))\n",
    "testScore = wMAPE(y_test, ypred_test)\n",
    "print('MAPE %.3f ' %(testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "4Qke2Du1w4o_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出测试集预测结果与真实值的对比折线图\n",
    "plt.figure(figsize=(12, 3))\n",
    "# 创建一个图形窗口，设置图形窗口的大小为宽度 12，高度 3\n",
    "plt.plot(y_test[1200:1700], label='True', color='r', linewidth=1) # 绘制 y_test 数据的折线图，并设置标签为 'Actual'\n",
    "plt.plot(ypred_test[1200:1700], label='Predicted', color='b' , linewidth=1, linestyle=\"--\") # 绘制 y_pred 数据的折线图，并设置标签为 'Predicted'\n",
    "plt.title('CNN Prediction', size=10)  # 设置图形的标题为'DBO-LSTM Prediction'，字体大小为10\n",
    "plt.ylabel('PV/MW', fontsize=10)  # 设置y轴标签为'PV（kWh）'，字体大小为10\n",
    "plt.xlabel('Sampling points', fontsize=10)  # 设置x轴标签为'Sampling points'，字体大小为10\n",
    "plt.legend()\n",
    "plt.savefig(\"TCN.png\")\n",
    "# 显示图例\n",
    "plt.show()\n",
    "\n",
    "# 显示图形窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pACCIDAMw7a8",
    "outputId": "d4c67dce-4cd5-4f5d-90c5-ef4806e436a6"
   },
   "outputs": [],
   "source": [
    "import xlrd\n",
    "import xlwt  #对xls文件进行改写\n",
    "from xlutils.copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vtJodmNww988",
    "outputId": "d83f12dc-a917-48f3-ec1a-68870692cafd"
   },
   "outputs": [],
   "source": [
    "# 新建表格\n",
    "def excel_create(path, sheet_name):\n",
    "    workbook = xlwt.Workbook(encoding = 'utf-8',style_compression = 0)  #相当于创建了一个EXCEL文件，style_compression:表示是否压缩，不常用)\n",
    "    workbook.add_sheet(sheet_name)  # 在工作簿中新建一个表格\n",
    "    workbook.save(path)  # 保存工作簿\n",
    "    \n",
    "# 写入表头\n",
    "def excel_write_title(path, title):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格，即index=0\n",
    "    for j in range(0, len(title)):\n",
    "        new_worksheet.write(0, j, str(title[j]))  # 在表格中第一行（row=0)写入标题\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格按列写入一维数组（列表）\n",
    "def excel_write_array(path, value, column):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        new_worksheet.write(i+1, column, float(value[i]))# 从第二行开始(row=i+1)，按对应的列(column)向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格写入二维数组（列表）\n",
    "def excel_write_array2(path, value):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        for j in range(len(value[0])):\n",
    "            new_worksheet.write(i+1, j, float(value[i][j]))# 从第二行开始(row=i+1)，按对应的行和列向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "8T-XP7dnzkQr"
   },
   "outputs": [],
   "source": [
    "path       = r'step2-TCN.xls' \n",
    "sheet_name = 'NOx'\n",
    "title      = ['TCN','y_test'] #表头\n",
    " \n",
    "excel_create(path, sheet_name) #新建表格\n",
    "excel_write_title(path, title) #写入表头\n",
    "#写入需要的一维数组(lat,lon,nox是之前已有的一维数组）\n",
    "excel_write_array(path, ypred_test,0)\n",
    "excel_write_array(path, y_test,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "qqqq",
   "language": "python",
   "name": "qqqq"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
